caa a22 4500 605468427 CHVBK 20210128100316.0 cr unu---uuuuu 210128e20150101xx s 000 0 eng 10.1007/s00500-014-1384-8 doi (NATIONALLICENCE)springer-10.1007/s00500-014-1384-8 Learning topic of dynamic scene using belief propagation and weighted visual words approach [Elektronische Daten] [Chunping Liu, Hui Lin, Shengrong Gong, Yi ji, Quan Liu] In this paper, we are tackling the problem of distinguishing scenes, including static and dynamic scenes. We propose a framework of scene recognition, based on bag of visual words and topic model. We achieve the task using the topic model by belief propagation (TMBP), which belongs to the family of the latent Dirichlet allocation model. We also extend the TMBP model, called as the knowledge TMBP model, by introducing the prior information of visual words and scenes. Experimental results on the static and dynamic scenes demonstrated that our proposed framework is effective and efficient. The scene semantics can be obtained from two levels of visual words and topics in our framework. Our result significantly outperforms the others using low-level visual features, such as spatial, temporal and spatiotemporal features. Springer-Verlag Berlin Heidelberg, 2014 Scene recognition nationallicence Topic model nationallicence Bag of visual words nationallicence Topic model by belief propagation (TMBP) nationallicence Liu Chunping School of Computer Science and Technology, Soochow University, 215006, Suzhou, China aut Lin Hui School of Computer Science and Technology, Soochow University, 215006, Suzhou, China aut Gong Shengrong School of Computer Science and Technology, Soochow University, 215006, Suzhou, China aut ji Yi School of Computer Science and Technology, Soochow University, 215006, Suzhou, China aut Liu Quan School of Computer Science and Technology, Soochow University, 215006, Suzhou, China aut Soft Computing Springer Berlin Heidelberg 19/1(2015-01-01), 71-84 1432-7643 19:1<71 2015 19 500 https://doi.org/10.1007/s00500-014-1384-8 text/html Onlinezugriff via DOI BK010053 XK010053 XK010000 Metadata rights reserved Springer special CC-BY-NC licence nationallicence 1 research-article jats NATIONALLICENCE NATIONALLICENCE NL-springer NATIONALLICENCE 856 40 https://doi.org/10.1007/s00500-014-1384-8 text/html Onlinezugriff via DOI NATIONALLICENCE 700 1- Liu Chunping School of Computer Science and Technology, Soochow University, 215006, Suzhou, China aut NATIONALLICENCE 700 1- Lin Hui School of Computer Science and Technology, Soochow University, 215006, Suzhou, China aut NATIONALLICENCE 700 1- Gong Shengrong School of Computer Science and Technology, Soochow University, 215006, Suzhou, China aut NATIONALLICENCE 700 1- ji Yi School of Computer Science and Technology, Soochow University, 215006, Suzhou, China aut NATIONALLICENCE 700 1- Liu Quan School of Computer Science and Technology, Soochow University, 215006, Suzhou, China aut NATIONALLICENCE 773 0- Soft Computing Springer Berlin Heidelberg 19/1(2015-01-01), 71-84 1432-7643 19:1<71 2015 19 500